Controlling noise with self-organized resetting

Area of Science:

• Complex Systems
• Statistical Physics
• Biophysics

Background:

• Biological systems with few components are prone to noise.
• Existing noise constraint mechanisms include external forces, nonlinear interactions, and state resetting.
• A new paradigm for noise constraint is needed.

Purpose of the Study:

• To introduce and investigate self-organized resetting as a novel mechanism for noise constraint.
• To explore the general properties of systems exhibiting self-organized resetting.
• To demonstrate the applicability of self-organized resetting in natural and technological contexts.

Main Methods:

• Studied general properties of self-organized resetting systems.
• Used cooperative resetting of Brownian particles as a paradigmatic example.
• Analyzed phase transitions and adaptive behaviors.

Main Results:

• Demonstrated a delocalization phase transition, distinguishing constrained and unconstrained noise regimes.
• Showed that self-organized resetting systems can adapt to external forces.
• Illustrated optimization of search behavior for target value attainment.

Conclusions:

• Self-organized resetting is a fourth paradigm for noise constraint, emerging from system interactions.
• This mechanism offers adaptability and optimized search capabilities.
• Applications span biological processes (fungal interactions) and technological systems (shared mobility).